This paper deals with stochastic dynamic facility layout problem under demand uncertainty in terms of material flow between facilities. A robust approach suggests a robust layout in each period as the most frequent one falling within a prespecified percentage of the optimal solution for multiple scenarios. Mont Carlo simulation method is used to randomly generate different scenarios. A mathematical model is established to describe the dynamic facility layout problem with the consideration of transport device assignment. As a solution procedure for the proposed model, an improved adaptive genetic algorithm with population initialization strategy is developed to reduce the search space and improve the solving efficiency. Different sized instances are compared with Particle Swarm Optimization (PSO) algorithm to verify the effectiveness of the proposed genetic algorithm. The experiments calculating the cost deviation ratio under different fluctuation level show the good performance of the robust layout compared to the expected layout.
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机译:本文在设施之间的材料流动方面处理了需求不确定性的随机动态设施布局问题。一种强大的方法在每个时段中提出了一种强大的布局,作为最常见的一个落入多种场景的最佳解决方案的预先限定百分比内。 Mont Carlo仿真方法用于随机生成不同的场景。建立了一种数学模型来描述传输设备分配的动态设施布局问题。作为所提出的模型的解决方案程序,开发了一种改进的具有人口初始化策略的自适应遗传算法,以减少搜索空间并提高解决效率。将不同大小的实例与粒子群优化(PSO)算法进行比较,以验证所提出的遗传算法的有效性。计算不同波动水平下的成本偏差比的实验表明,与预期布局相比,鲁棒布局的良好性能。
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